class: center, middle, inverse, title-slide .title[ # <div class="line-block">Here Comes the STRAIN: <br> Analyzing Defensive Pass Rush in American Football with Player Tracking Data</div> ] .subtitle[ ## Quang Nguyen ] .institute[ ### Department of Statistics & Data Science <br> Carnegie Mellon University <br> <br> 2023 NESSIS ] .date[ ### <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"/></svg> <span class="citation">@qntkhvn</span>       <svg aria-hidden="true" role="img" viewBox="0 0 496 512" style="height:1em;width:0.97em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg> qntkhvn       <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M579.8 267.7c56.5-56.5 56.5-148 0-204.5c-50-50-128.8-56.5-186.3-15.4l-1.6 1.1c-14.4 10.3-17.7 30.3-7.4 44.6s30.3 17.7 44.6 7.4l1.6-1.1c32.1-22.9 76-19.3 103.8 8.6c31.5 31.5 31.5 82.5 0 114L422.3 334.8c-31.5 31.5-82.5 31.5-114 0c-27.9-27.9-31.5-71.8-8.6-103.8l1.1-1.6c10.3-14.4 6.9-34.4-7.4-44.6s-34.4-6.9-44.6 7.4l-1.1 1.6C206.5 251.2 213 330 263 380c56.5 56.5 148 56.5 204.5 0L579.8 267.7zM60.2 244.3c-56.5 56.5-56.5 148 0 204.5c50 50 128.8 56.5 186.3 15.4l1.6-1.1c14.4-10.3 17.7-30.3 7.4-44.6s-30.3-17.7-44.6-7.4l-1.6 1.1c-32.1 22.9-76 19.3-103.8-8.6C74 372 74 321 105.5 289.5L217.7 177.2c31.5-31.5 82.5-31.5 114 0c27.9 27.9 31.5 71.8 8.6 103.9l-1.1 1.6c-10.3 14.4-6.9 34.4 7.4 44.6s34.4 6.9 44.6-7.4l1.1-1.6C433.5 260.8 427 182 377 132c-56.5-56.5-148-56.5-204.5 0L60.2 244.3z"/></svg> qntkhvn.netlify.app ] --- <!-- layout: true --> <!-- <div class="my-footer"><span></span></div> --> --- count: false <img src="data:image/png;base64,#rubber.png" width="64%" style="display: block; margin: auto;" /> --- # football meets materials science <img src="data:image/png;base64,#analogy2.png" width="110%" style="display: block; margin: auto;" /> --- count: false # football meets materials science <img src="data:image/png;base64,#analogy3.png" width="110%" style="display: block; margin: auto;" /> --- count: false # football meets materials science <img src="data:image/png;base64,#analogy4.png" width="110%" style="display: block; margin: auto;" /> --- # measuring pressure at the frame-level **Definition** ( `\(\text{STRAIN}\)`; informal) For every pass rusher at each frame within a play, calculate -- * `\(d\)`: distance between pass rusher and QB -- * `\(v\)`: velocity at which pass rusher is moving toward QB -- * estimated by rate of change in distance between current & previous frames -- * `\(\text{STRAIN} = \displaystyle- \frac{v}{d}\)` --- # Advantages of STRAIN * Simple, computation friendly: 2 features! #PutThatInYourXGBoost -- * Interpretable: 1/STRAIN <br> `\(\rightarrow\)` time required for a pass rusher to get to QB with current velocity & distance -- * Scalable: can be applied to every passing play (minus trick plays) -- * Continuous-time within-play metric * Previous metrics are either discrete or based on subjective judgment --- count: false class: center, middle <p style="font-size:2.5em; color: #C41230"> <b> Real-game illustration<br>of STRAIN </b> </p> --- # data: nfl big data bowl 2023 Player tracking data for the first 8 weeks of the 2021 regular season Example play: Raiders vs Broncos (week 6) – T. Bridgewater sacked by M. Crosby <img src="data:image/png;base64,#track.png" width="80%" style="display: block; margin: auto;" /> --- # example play for Raiders DE Maxx Crosby <img src="data:image/png;base64,#play_ini.gif" width="85%" style="display: block; margin: auto;" /> --- <img src="data:image/png;base64,#ex_play.gif" width="62%" style="display: block; margin: auto;" /> --- count: false class: center, middle <p style="font-size:2.5em; color: #C41230"> <b> Statistical properties<br>of STRAIN </b> </p> --- # Positional strain curves <img src="data:image/png;base64,#pos.png" width="70%" style="display: block; margin: auto;" /> --- # play outcome strain curves <img src="data:image/png;base64,#outcomes.png" width="99%" style="display: block; margin: auto;" /> --- # STRAIN is predictive of pressure <img src="data:image/png;base64,#cor_pressure.png" width="60%" style="display: block; margin: auto;" /> --- # STRAIN is more predictive of pressure than pressure itself <img src="data:image/png;base64,#predictability.png" width="100%" style="display: block; margin: auto;" /> --- # STRAIN is highly stable over time <img src="data:image/png;base64,#stability.png" width="60%" style="display: block; margin: auto;" /> --- count: false class: center, middle <p style="font-size:2.5em; color: #C41230"> <b> Multilevel model for<br>play-level STRAIN </b> </p> --- # Multilevel model: Specification - Response: average STRAIN for pass rusher at the play-level - Random effects: pass rusher, pass blocker, defensive team, offensive team - Fixed effects: number of blockers, position of blocker and rusher, play-context (down, yards to go, current yardline) `$$\small \begin{aligned} \overline{\text{STRAIN}}_{ij} &\sim N(R_{j[i]} + B_{b[ij]} + D_{d[i]} + O_{o[i]} + \mathbf{x_{ij}} \boldsymbol{\beta}, \sigma^2); \text{ } i = 1, \dots, n \text{ plays} \\ R_{j} &\sim N(\mu_R, \sigma^2_R); \text{ } j = 1, ..., \text{# of rushers} \\ B_{b} &\sim N(\mu_B, \sigma^2_B); \text{ } b = 1, ..., \text{# of blockers} \\ D_{d} &\sim N(\mu_D, \sigma^2_D); \text{ } d = 1, ..., \text{# of defenses} \\ O_{o} &\sim N(\mu_O, \sigma^2_O); \text{ } o = 1, ..., \text{# of offenses} \end{aligned}$$` --- # Multilevel model: Resampling -- - Capture uncertainty in the estimates for the player random effects -- - It's unrealistic to bootstrap individual plays -- - Instead, bootstrap team drives within games --- # Multilevel model: Pass rusher rankings <img src="data:image/png;base64,#boot_rank.png" width="76%" style="display: block; margin: auto;" /> --- # summary and future work <!-- #HereComesTheSTRAIN --> Introducing STRAIN: a simple and interpretable pass rush metric inspired by materials science - Continuous-time within-play measure of defensive pressure - Great predictability of pressure and stability over time - Identifies top-level pass rushers -- Next steps - How about pass block? - Player STRAIN curves as functions --- # acknowledgements: ron and greg .pull-left[ <img src="data:image/png;base64,#https://pbs.twimg.com/profile_images/1614452588258230273/0QToRrz__400x400.jpg" width="60%" style="display: block; margin: auto;" /> <center> Ronald Yurko<br>Carnegie Mellon University </center> ] .pull-right[ <img src="data:image/png;base64,#https://raw.githubusercontent.com/qntkhvn/qblog/main/projects/greg.gif" width="64%" style="display: block; margin: auto;" /> <center> Gregory J. Matthews<br>Loyola University Chicago </center> ] --- # acknowledgements: big data bowl .pull-left[ <img src="data:image/png;base64,#bdb.png" width="60%" style="display: block; margin: auto;" /> ] .pull-right[ <br> <br> <center><big><b><font color="#C41230">Check out<br>#BigDataBowl 2024!</font></b></big></center> ] --- # Related links * Paper: [arxiv.org/abs/2305.10262](https://arxiv.org/abs/2305.10262) * GitHub: [github.com/getstrained](https://github.com/getstrained) -- <p style="font-size:1.2em; color: #C41230"> <b> Follow the #STRAINtrain to #CMSAC (November 10—11, 2023) </b></p> * Register at [stat.cmu.edu/cmsac/conference/2023](https://www.stat.cmu.edu/cmsac/conference/2023) * Featuring a Big Data Bowl "behind the scenes" workshop on STRAIN -- <center> <p style="font-size:1.25em; color: #C41230"> <b> Cheers. </b> </p> </center>